Pixel level fusion of panchromatic and multispectral images based on correspondence analysis

A pixel level data fusion approach based on correspondence analysis (CA) is introduced for high spatial and spectral resolution satellite data. Principal component analysis (PCA) is a well-known multivariate data analysis and fusion technique in the remote sensing community. Related to PCA but a more recent multivariate technique, correspondence analysis, is applied to fuse panchromatic data with multispectral data in order to improve the quality of the final fused image. In the CA-based fusion approach, fusion takes place in the last component as opposed to the first component of the PCA-based approach. This new approach is then quantitatively compared to the PCA fusion approach using Landsat ETM+, QuickBird, and two Ikonos (with and without dynamic range adjustment) test imagery. The new approach provided an excellent spectral accuracy when synthesizing images from multispectral and high spatial resolution panchromatic imagery.